{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# 为什么一定要掌握自学能力？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "一句话解释清楚：\n",
    "\n",
    "> 没有自学能力的人没有**未来**。\n",
    "\n",
    "有两个因素需要深入考虑：\n",
    "\n",
    "> * 未来的日子还很长\n",
    "> * 这世界进步得太快\n",
    "\n",
    "我有个观察：\n",
    "\n",
    "> 很多人都会不由自主地去复刻父母的人生时刻表。\n",
    "\n",
    "比如，你也可能观察到了，父母晚婚的人自己晚婚的概率更高，父母晚育的人自己晚育的概率也更高…… \n",
    "\n",
    "再比如，绝大多数人的内心深处，会不由自主地因为自己的父母在五十五岁的时候退休了，所以就默认自己也会在五十五岁前后退休…… 于是，到了四十岁前后的时候就开始认真考虑退休，在不知不觉中就彻底丧失了斗志，早早就活得跟已经老了很多岁似的。\n",
    "\n",
    "但是，这很危险，因为很多人完全没有意识到自己所面临的人生，与父母所面临的人生可能完全不一样 —— 各个方面都不一样。单举一个方面的例子，也是比较容易令人震惊的方面：\n",
    "\n",
    "> 全球范围内都一样，在过去的五十年里，人们的平均寿命预期增长得非常惊人……\n",
    "\n",
    "拿中国地区做例子，根据世界银行的数据统计，中国人在出生时的寿命预期，从 1960 年的 _47.73_ 岁，增长到了 2016 年的 _76.25_ 岁，56 年间的增幅竟然由 **74.39%** 之多！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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4cGqLVqp9Fn4QX0bwAb45oo7/Agj341kEH9ZlBIPcz43YxPkE7+XvCMYLVQN/jLh/l/uDoBvxnX19DWHYOI3g92IdQffZl9pY/0mCVqy/mdk2ghbX5rnRXiIINp8QdBnWsHN35ueAxWZWQdDqdG44Bm09wRQr1xEEuPXA1Xz6efVlgn1XStD1/tc9eY0WDPyf1fJxu/t/EQauCvv0QJaJBEdwVhAcNbocuDRik+cTBNpC4DiCLwi1Lcq5jaCLuXlc2v+Eta0HnnFNVdHl2c5fBEREpLOxYPLZx9z9pd2uLCIdhkKYiIiISByoO1JEREQkDhTCREREROJAIUxEREQkDhTCREREROIg2tN2xNWAAQN8+PDh8S5DREREZLfmz59f7O5Zu1uvU4Sw4cOHM2+epksRERGRjs/MojqdlrojRUREROJAIUxEREQkDhTCREREROJAIUxEREQkDhTCREREROJAIUxEREQkDhTCREREROJAIUxEREQkDhTCREREROKgU8yYLyIiIrInGpucTeXVrCutYl1JFetKq8gvreLSo0YydVi/eJcHKISJiIhIJ1VR28D60iryS6pYX/pp0FpfWsWGsirqG33Huj0SjJyMPpRW1sax4p0phImIiEiH1NTkbNlew7qST8PVuojQVVJZt9P66X16kpeZzMQhaXxu0iBy+yeT1z+ZYf2TGZyeRI/EjjUKSyFMRERE4qa2oZENZdVB0CqpJD/sPmwOXbUNTTvWTTAY0q8PeZnJnHjAQHL7p5DbP3nHJT25ZxxfyZ5TCBMREZGYqq5rZF1pFWtLKskvqWRtGLjWFlexqbyapk97DenTM5G8zGRGDkjh2HFZ5Gam7GjRGprRh54drDVrXyiEiYiIyD6rbWhkfWkVq4sqWVMcXFYXB6Fry7adx2FlJPckLzOFg4dnkJuZQ17/ZPIyk8nNTCarb2/MLE6von0phImIiEhU3J3N22pYVVjJqqKKHUFrTXEFG8t2btEa0LcXwzNTOGpMFsMzk8nLTCEvM5m8/imdrtswVmIWwsxsHPBIxKKRwI1AP+BSoChcfp27Px+rOkRERGTP1Dc2kV9SxcrCClYVVbCqsIKV4c/KusYd66X0SmREVgpTh2Vw5rQcRg5IYcSAFIYPSCG9j4LW7sQshLn7cmAqgJklAhuBJ4GLgN+4+22xem4RERHZvcYmJ7+kkk+2VPDJlu0s37KdTzZvZ01xJQ0RzVqD0pIYnd2Xs6fnMDq7L6Oy+jIquy/Zqd2n6zAW2qs78jhglbvn680SERFpf4Xba1hSsI2lm7azIgxcKwsrdjr6MLd/MmMHpnL8xIGMiQhbfXtr9FIstNdePRd4OOL2FWZ2ATAP+J67l7VTHSIiIl1aY5OztqSSxQXbWFKwjSWbgp/FFZ8Ojh+cnsSYgakcPiqTsQNTGTcoldHZfUnupbDVnszdd7/WvjyBWS+gADjA3beY2UCgGHDgJ8Bgd/9aK4+bDcwGyM3NnZ6fnx/TOkVERDqbxiZnVVEFC9ZvZeGGchYVlLNs03aq64NxWz0TjTHZqUwcksbEwWkcMCSN8YPSNDA+xsxsvrvP2N167RF5TwI+cPctAM0/Aczsj8BzrT3I3e8G7gaYMWNGbJOiiIhIB+furCutYsGGchZGhK6qcKB83949OGBIGufNzN0RukZn96VXj64zr1ZX0x4h7DwiuiLNbLC7bwpvngksaocaREREOpWK2gYWrN/K/Pwy5ueX8dH6rZRX1wPQu0cCE4ek8cUZw5ick87knH6MHJBCQoLGXXcmMQ1hZpYMnABcFrH4F2Y2laA7cm2L+0RERLodd2dDWTUfrAsC17y1ZSzbvI0mBzMYNzCVkw8cxOScfkzOSWfswNQuNXN8dxXTEObuVUBmi2Xnx/I5RUREOjp3Z3VxJe+sKuGd1SW8v6aUwu3BwPmUXolMy83gilljmJ6XwdRh/TTnVhelwyBERERirHk8V3PoemdVyY7QNTg9icNGZTIjL4Ppef0ZNyiVRHUrdgsKYSIiIjFQUlHLmyuKeGtFCe+uLmHj1moABvTtzWGjMjl8VCaHjcwkLzNZE552UwphIiIi+0FTk7OooJzXlhXx2vJCFmzYintwsupDR2Zy2dEjOXxUJqOy+ip0CaAQJiIistfKq+v514oiXltWxBufFFJcUYcZTMnpx7ePG8ux47OYNCRdRy1KqxTCRERE9sCm8mpeXLSZFxdtZl5+GY1NTnqfnhw9Notjx2fxmTFZZPbtHe8ypRNQCBMREdmN/JJKXli0mRcWbWbB+q0AjB3Yl8uPHsms8dlMyelHD00ZIXtIIUxERKQFd2dFYQUvfLyZFxZtYtnm7QAcODSdqz87js9NGsSorL5xrlI6O4UwERGR0JriSp75qIBnFmxkVVElZjAjL4MbTpnA5yYNIicjOd4lSheiECYiIt3a5vIanltYwDMLCli4oRwzOGREf756xAg+O3Eg2WlJ8S5RuiiFMBER6Xa2VtXxwqLNPPNRAe+uKcE96Gq84ZQJnDp5CIPSFbwk9hTCRESkW6htaOTVpYX8/YONvPFJIfWNzsgBKXzruDGcPmUIIzXGS9qZQpiIiHRZ7s6H67fy9/kbeG7hJsqr68lO7c2Fhw3njGlDOWBImiZOlbhRCBMRkS5nfWkVT324kSc+3Mia4kqSeibwuQMG8YWDcjhi9ACdm1E6BIUwERHpEipqG3j+4038ff4G3ltTCsChI/vz38eM4uQDB9O3tz7ypGPRb6SIiHRaTU3Ou6tLeHz+Bl5YtJnq+kZGDEjhqhPHcsa0oZpSQjo0hTAREel01hZX8vcPNvDEBxvZuLWa1KQenHnQUM46KIeDcvtpnJd0CgphIiLSKWyrqef5hZt4fP4G5uWXkWBw1JgsrjlpPCdMHEhSz8R4lyiyRxTCRESkw2pobOJfK4p54sONvLJkMzX1TYzO7ssPPjeeM6cN1Xxe0qkphImISIfi7iwu2MbfP9jAswsKKK6oo19yT86ZPoyzpucwJSdd3Y3SJSiEiYhIh1CwtZqnPtrIkx9sZEVhBb0SEzhuQjZnThvKMeOy6dUjId4liuxXCmEiIhI3ZZV1vLh459MHzcjL4KdnTuLUA4eQntwz3iWKxIxCmIiItKvy6npeXryZ5xZu4u2VxTQ0OSPC0wedOW0oeZkp8S5RpF0ohImISMxV1DYwZ+kWnl1QwJufFFPX2ERORh8uOWokp04erNMHSbekECYiIjFRXlXPa8sLeWnxZl5dVkhtQxOD0pI4/7A8Tp08mKnDNJ+XdG8KYSIist+sL63in0u38MqSLby3ppTGJicrtTdfOngYp04ewoy8DBJ03kYRQCFMRET2QfN0Ei8vCYLX0k3bABid3ZfZnxnJCRMHMjWnn4KXSCsUwkREZI+UVNQyd1UJb60o5s0VRWwqryHBYHpeBtedPJ4TJg5ixAANrhfZHYUwERFpU019I++vLeWtFcW8tbKYxQVBa1daUg8OHzWA75yQzXHjs8ns2zvOlYp0LgphIiKyk9qGRhZtLOffa8p4a2UR768to66hiZ6JxkG5GVx14liOGD2AyTn9SFQ3o8hei1kIM7NxwCMRi0YCNwJ/DZcPB9YCX3T3sljVISIibSvaXsv8/DI+WFfG/PwyPt5QTl1jEwDjBqZy/qF5HDlmADOH9yelt767i+wvMftrcvflwFQAM0sENgJPAtcAc9z9VjO7Jrz9g1jVISIin6qpb2T55u18vLGcD/LLmL+ujPySKgB6JSZwYE46Xz1iOAflZjA9L4OsVHUxisRKe32lOQ5Y5e75ZvZ54Jhw+X3A6yiEiYjsV+5O0fZalmzaxpJN21i6aTtLCspZU1xJkwfrDOjbm+l5/fivQ3KZntefSUPT6N0jMb6Fi3Qj7RXCzgUeDq8PdPdNAO6+ycyy26kGEZEupzlsrSyqYHVRJauKKlhZWMGSgm2UVNbtWG9ovz5MGJzGKZOHMHFwKhMHpzOsfx9NlioSRzEPYWbWCzgduHYPHzcbmA2Qm5sbg8pERDqPqroG8kuqyC+pZFVRJasKK1hVXMnqwgq21zbsWK9Pz0RGZacwa3w2E4ekMWFwGhMGpelE2CIdUHu0hJ0EfODuW8LbW8xscNgKNhgobO1B7n43cDfAjBkzvB3qFBGJG3entLKOtSVVrCutJL+kinUlVeSXVpFfUkVxRe1O6w9OT2JkVgpnHjSUUVl9GZmVwqisvgxKS9LEqCKdRHuEsPP4tCsS4BngQuDW8OfT7VCDiEhcuTvl1fVsKKtmQ1kV60uDnxvKqlkf/qyqa9zpMYPTk8jtn8ys8VnkZaaQ2z+Z4ZkpjMhKoa+OUhTp9GL6V2xmycAJwGURi28FHjWzi4F1wDmxrEFEpD00NTlFFbVs3FrNxrLqnX4WhNcjuw0BUnv3IKd/MnmZKRwxegDDMpLJywwuORnJJPXUIHmRriymIczdq4DMFstKCI6WFBHpNGobGtlcXsPGsmo2hKGqYGsYtrZWs2lrzY65tZqlJfVgaEYQqA4dmUlORh9yMpLJyejDsIxkjdMS6ebUni0iQjB/VnNXYfAzCFcbyqrYWFZNUUUt3mJ0anZqb4Zm9OHAoel8btIghvbrE1wygp+pSQpZIrJrCmEi0i00NDZRsLWGdaVVOy7NY7E2llVRXFG30/o9E40hYag6emzWjmDVHLIGpSdpTi0R2ScKYSLSZdTUN5JfUsXqooodRxWuDwPXxq3VNDZ92pTVKzGBoRl9yMnow8SJA3d0Ew7tF3QZZqf21lGGIhJTCmEi0qm4O5u31bC6qJLVRRWsLq4MrhdXsKGseqcuw/4pvRjWP5mpw/px+pQh5GYmk9s/uGgqBxGJN4UwEemwtlbVsWzzdpZv3h7+3MYnWyqoiDjKMLlXIiMGpDB1WAZfmJazY76svMxkjckSkQ5NIUxE4q6xyVlTXMHHG8tZuunTwLVl26cTlPZL7sm4gamcddBQRmf3ZWQ4QemgtCSdekdEOqXdhjAzOwL4EZAXrm+Au/vI2JYmIl1RU5OztqSSjzeWs3BDOR9vLGfxxnIqw4lKe/VIYHRWX44YPYDxg1IZNyiN8YNSyU7trbAlIl1KNC1h9wDfAeYDjbtZV0RkJyUVtczPL2P+ujIWri9n0cbyHZOW9u6RwAFD0jh7eg4H5vTjwKHpjMpKoUdiQpyrFhGJvWhCWLm7vxDzSkSk03N31hRXMi+/jHlrS5mXX8bqokogOBpxwpA0zpg2lAOHpnNgTjpjsvsqcIlIt7XLEGZmB4VXXzOzXwJPADsGaLj7BzGuTUQ6uKYmZ+nmbbyzqoR/ryllfn4ZJZXBfFv9knsyIy+Dc6YP4+DhGUwamq7T8IiIRGirJexXLW7PiLjuwKz9X46IdHTrS6t4a2Uxb60s5p1VJZSGoWt4ZjLHjMtmxvAMDh6ewcgBfTUFhIhIG3YZwtz9WAAzG+nuqyPvMzMNyhfpJkor65i7qpi3w+C1vrQagIFpvTlmXBZHjh7A4aMGMCg9Kc6Vioh0LtGMCXscOKjFsseA6fu/HBGJN3dnRWEFryzZwpylW/hw/VbcITWpB4eNzOSSI0dyxOgBjMpK0dGKIiL7oK0xYeOBA4B0M/tCxF1pgL7yinQh9Y1NvL+mlFeWbmHO0kLWlVYBMDknnW8fN5bPjB3AgUPTNYheRGQ/aqslbBxwKtAPOC1i+Xbg0lgWJSKxV1nbwJxlhfxzyRZeW17I9poGevVI4MjRA7j86FEcNyGbgWn6viUiEittjQl72syeA37g7j9rx5pEJEZq6ht5fXkRzy4oYM6yLdTUN5GZ0ouTJg3i+AkDOXLMAJJ76UQaIiLtoc3/tu7eaGYnAAphIp1UfWMTc1eV8MxHBby8eDPbaxvITOnFOdOHcdqUIUzPyyBRRzGKiLS7aL7yzjWzu4BHgMrmhZonTKTjcnfeX1vGMws28vzHmymtrCO1dw8+O2kQp08ZwuGjMjW+S0QkzqIJYYeHP2+OWKZ5wkQ6oMJtNTw2fwOPzltPfkkVST0TOG7CQE6fMoSjx2ZpslQRkQ5ktyGseb4wEemYGhqbeOOTIv72/npeXVZIY5Mzc0R/vjlrDJ+bNIiU3hr8Z60SAAAgAElEQVTjJSLSEbU1RcVX3P0BM/tua/e7+69jV5aI7M66kioenbeex+avZ8u2Wgb07c0lR43gSzOGMTKrb7zLExGR3WjrK3JK+DO1PQoRkd1rbHJeWbKF+99dy9srS0gwOGZcNjd/fhizxmfTU+O8REQ6jbamqPhD+PPH7VeOiLRmW009j76/nnvnrmVDWTVD+/XhuyeM5ezpOQzp1yfe5YmIyF5oqzsyCfgSUAY8C1wNfAZYBfzE3YvbpUKRbmxNcSX3zV3LY/PWU1nXyMzh/bn+5AmcMHGgjm4UEenk2uqO/CtQT9At+T1gEXAXcCRwL8Fs+iKyn7k7c1eV8Oe31vDq8kJ6JBinTR7CRUeM4MCc9HiXJyIi+0lbIWyiu08ysx7ABnc/Olz+opktaIfaRLqVhsYmnl1YwB/eWM2yzdvJTOnFlbPG8JVDcsnW6YNERLqctkJYHYC7N5hZQYv7GmNXkkj3UtfQxBMfbOD/Xl/FutIqxg1M5RdnT+b0KUM0r5eISBfWVgjLMbM7AIu4Tnh7aMwrE+niauobeeT99fzhjVUUlNcwOSedG06ZzvETBpKg0wiJiHR5bYWwqyOuz2txX8vbIhKlqroGHnx3HXf/azVF22uZkZfB/5w1mc+MGYCZwpeISHfR1hQV9+3rxs2sH/AnYBLBqY6+BnwWuBQoCle7zt2f39fnEunoKmobuG/uWv70r9WUVdVzxOhM7jh3GoeO7K/wJSLSDcX6fCa3Ay+6+9lm1gtIJghhv3H322L83CIdQl1DEw+9l8+dr66kpLKOWeOz+caxo5melxHv0kREJI5iFsLMLI1gXrGvArh7HVCnb/zSXbg7zy3cxG0vLye/pIpDR/bnnpMmMHVYv3iXJiIiHUAsW8JGEnQ5/sXMpgDzgW+F911hZhcQjC37nruXxbAOkXY3d1Uxt76wjIUbyhk/KJW/XHQwx4zNUrejiIjsYO7e9gpmI4ArgeFEhDZ3P303j5sBvAsc4e7vmdntwDaCCV+LCcaI/QQY7O5fa+Xxs4HZALm5udPz8/Ojf1UicbJs8zZufWEZry8vYkh6Et89cRxnThtKoo52FBHpNsxsvrvP2N160bSEPQXcQ3DqoqY9qGEDwSSv74W3HweucfctEUX+EXiutQe7+93A3QAzZsxoOymKxNnm8hp++dJynvhwA6m9e3DtSeO58PDhmudLRER2KZoQVuPud+x+tZ25+2YzW29m49x9OXAcsMTMBrv7pnC1MwlOhyTSKdU1NPHnt9dwx5wVNDQ5lx41kq8fM4p+yb3iXZqIiHRw0YSw283sJuBloLZ5obt/EMVjrwQeDI+MXA1cBNxhZlMJuiPXApftadEiHcHbK4u58elFrCqq5PgJA7nptIkM658c77JERKSTiCaEHQicD8zi0+5ID2+3yd0/Alr2iZ6/JwWKdDSby2u45R9LeG7hJnL7J/Pnr85g1viB8S5LREQ6mWhC2JnAyHCKCZFuq66hib+8vYbb56ygscn5zvFjuezokRr3JSIieyWaELYA6AcUxrgWkQ5r7spibnxmMSsLK9T1KCIi+0U0IWwgsMzM3mfnMWFtTlEh0hWUVdZx83NLePLDjeT2T+aeC2dw3AR1PYqIyL6LJoTdFPMqRDqg5z/exI1PL2JrVT3fPG4MXz9mlLoeRURkv9ltCHP3N8xsIHBwuOjf7q6uSemyCrfXcONTi3lx8WYOHJrO/RcfwoTBafEuS0REupjdhjAz+yLwS+B1wIA7zexqd388xrWJtCt354kPNnLzc0uorm/kmpPGc8mRI+iRmBDv0kREpAuKpjvyeuDg5tYvM8sC/kkwA75Il1CwtZrrnvyY15cXMT0vg1+cPZlRWX3jXZaIiHRh0YSwhBbdjyWAmgakS2hqch5+fx3/8/wyGpucm06byAWHDde5HkVEJOaiCWEvmtlLwMPh7S8Bz8euJJH2sWVbDVc9toB/rSjm8FGZ3PqFyeRmatoJERFpH9EMzL/azL4AHEkwJuxud38y5pWJxNArS7bw/ccXUF3fyC1nTOK/DsnFTK1fIiLSftoMYWaWCLzk7scDT7RPSSKxU13XyC3/WMKD763jgCFp3H7uNEZna+yXiIi0vzZDmLs3mlmVmaW7e3l7FSUSC0sKtvHNv33IysIKZn9mJN87cSy9e2jeLxERiY9oxoTVAB+b2StAZfNCd/9mzKoS2Y+ampw/v72GX7y4nH7JPbn/4pkcNSYr3mWJiEg3F00I+0d4Eel0CrfX8L1Hg8H3x08YyC/Onkz/lF7xLktERGTXIczM5rj7ccBEd/9BO9Yksl+8tryQqx5dQGVdgwbfi4hIh9NWS9hgMzsaON3M/kZwZOQO7v5BTCsT2UtNTc7tc1Zw+5wVTBicxp3nTWV0dmq8yxIREdlJWyHsRuAaIAf4dYv7HJgVq6JE9lZ5VT3ffuRDXltexNnTc7jljEk66baIiHRIuwxh4bkhHzezH7r7T9qxJpG9sqRgG5c/MJ9N5dXqfhQRkQ4vmslaFcCkw3vyww1c+8TH9OvTi0cuO4yDcjPiXZKIiEibojk6UqTDqmto4mfPL+XeuWs5ZER/7vryQWSl9o53WSIiIrulECadVuG2Gr7+4AfMyy/jkiNHcM1J4+mRqHPLi4hI57DbEGZmtwF/cffF7VCPSFTmrS3lvx/8gMraBu48bxqnTRkS75JERET2SDQtYcuAu82sB/AX4GGdwkji6ZkFBVz16AKGZvThwUsOYexATT8hIiKdz277btz9T+5+BHABMBxYaGYPmdmxsS5OJJK787vXV/HNhz9kam4/nvr6EQpgIiLSaUU1gMbMEoHx4aUYWAB8N5zEVSTmGhqbuP6pRfz8xWWcPmUI9188k/TknvEuS0REZK9FMybs18DpwBzgZ+7+7/Cun5vZ8lgWJwJQWdvAFQ99wGvLi/j6MaO46sRxJCRo/i8REencohkTtgi4wd2rWrlv5n6uR2Qnhdtq+Np977OkYBs/PXMS/3VIXrxLEhER2S+iCWFlwI5+HzPrBxzj7k9pgL7E0idbtnPRX96nrKqOey48mGPHZ8e7JBERkf0mmjFhN0WGLXffCtwUu5JEYO6qYs763VzqGpt49LLDFMBERKTLiSaEtbZOVJO8mlk/M3vczJaZ2VIzO8zM+pvZK2a2Ivyp88vITp7+aCMX/vnfDEpL4smvH86koenxLklERGS/iyaEzTOzX5vZKDMbaWa/AeZHuf3bgRfdfTwwBVgKXAPMcfcxBIP9r9mbwqVrevLDDXz7kY84KDeDx//7cHIykuNdkoiISExEE8KuBOqAR4DHgBrgG7t7kJmlAZ8B7gFw97qwK/PzwH3havcBZ+x52dIVPbuggO89uoBDR2Ry70UzSe+jKShERKTr2m23ortXsnetVSOBIuAvZjaFoPXsW8BAd98UbnuTmWmwj/DCx5v49iMfMSOvP/d8dQZ9eiXGuyQREZGYimaesLHAVQSz5e9Y391nRbHtg4Ar3f09M7udPQhzZjYbmA2Qm5sb7cOkE3plyRaufPhDpuSk8+eLDia5l84rLyIiXV80n3aPAb8H/gQ07sG2NwAb3P298PbjBCFsi5kNDlvBBgOFrT3Y3e8G7gaYMWOG78HzSify2rJCvv7gfA4Yms69X5tJ394KYCIi0j1E84nX4O6/29MNu/tmM1tvZuPcfTlwHLAkvFwI3Br+fHpPty1dw5ufFHHZA/MZNyiVv35tJmlJGgMmIiLdRzQh7Fkz+zrwJFDbvNDdS6N47JXAg2bWC1gNXERwMMCjZnYxsA44Z4+rlk5v7spiLv3rPEZl9eWBiw/RIHwREel2oglhF4Y/r45Y5gQD79vk7h8BM1q567gonle6qPdWl3DxffPIy0zmgYtn0i+5V7xLEhERaXfRHB05oj0Kke5h3tpSLrr3fYb0S+LBSw4ls2/veJckIiISF9HOfD8JmAgkNS9z97/GqijpmtYWV/K1e99nYFoSD196KFmpCmAiItJ9RTNFxU3AMQQh7HngJOAtQCFMolZR28Ds++eRkGDcd9FMstOSdv8gERGRLiyaGfPPJhjDtdndLyI4/ZCaMCRqTU3O9x79iJWFFdx13kHkZupURCIiItGEsGp3bwIawlMRFRLFoHyRZr99bSUvLd7CdSdP4MgxA+JdjoiISIcQzZiweWbWD/gjwamHKoB/x7Qq6TL+uWQLv/7nJ5wxdQgXH6ljPERERJpFc3Tk18OrvzezF4E0d18Y27KkK1hZWMF3HvmIA4akcetZkzGzeJckIiLSYey2O9LM5jRfd/e17r4wcplIa7bV1DP7/nn06pHAH86fQVJPnZBbREQk0i5bwswsCUgGBphZBtDcjJEGDGmH2qSTampyvvO3j1hXUsUDlxzC0H594l2SiIhIh9NWd+RlwLcJAtd8Pg1h24Dfxrgu6cT+95+fMGdZITd//gAOHZkZ73JEREQ6pF2GMHe/HbjdzK509zvbsSbpxF5ctIk7Xl3JF2fkcP6hefEuR0REpMOKZoqKpvDoSADMLCM8obfITpZv3s53H13A1GH9uPnzkzQQX0REpA3RhLBL3X1r8w13LwMujV1J0hlV1TVw+QPzSendg99/ZboG4ouIiOxGNCEswSKaNMwsEegVu5KkM/r5C8tYU1zJHedOY1C6TkkkIiKyO9FM1voS8KiZ/R5w4HLgxZhWJZ3K2yuLue+dfC46YjiHjdJAfBERkWhEE8J+QHCk5H8THCH5MvCnWBYlncf2mnq+//hCRgxI4fufHR/vckRERDqNaGbMbzKze4FX3X157EuSzuSW55ayqbyaxy4/nD69NA5MREQkWtHMmH868BFhF6SZTTWzZ2JdmHR8ry0r5JF565n9mVFMz8uIdzkiIiKdSjQD828CZgJbAdz9I2B4DGuSTmBrVR0/+PtCxg7sy3dOGBPvckRERDqdaEJYg7uXx7wS6VR+9MxiSivr+PUXp9K7h7ohRURE9lQ0IWyRmX0ZSDSzMWZ2JzA3xnVJB/biok089VEB3zh2NJOGpse7HBERkU4pmhB2JXAAUAs8THDuyG/HsijpuEoqarn+yUUcMCSNK2aNjnc5IiIinVY0R0dWAdeb2c+Dm7499mVJR+Tu3PDUIrbXNPDQpVPpmRhNhhcREZHWRHN05MFm9jGwEPjYzBaY2fTYlyYdzTMLCnhh0Wa+c8JYxg1KjXc5IiIinVo0k7XeA3zd3f8FYGZHAn8BJseyMOlYtmyr4canFzMttx+zPzMy3uWIiIh0etH0J21vDmAA7v4WoC7JbsTdue6Jj6mpb+S2c6aQmGC7f5CIiIi0KZqWsH+b2R8IBuU78CXgdTM7CMDdP4hhfdIBvLR4M3OWFXLDKRMYldU33uWIiIh0CdGEsKnhz5taLD+cIJTN2q8VSYdSXdfIT55byvhBqXz18OHxLkdERKTLiOboyGPboxDpmH772ko2bq3mkdmH0kNHQ4qIiOw30Rwdeb+ZpUfczjOzOdFs3MzWmtnHZvaRmc0Ll/3IzDaGyz4ys5P3vnyJpTXFldz95mrOmDqEQ0ZmxrscERGRLiWa7si3gPfM7LvAUOBq4Ht78BzHuntxi2W/cffb9mAb0s7cnR8/u5hePRK47uQJ8S5HRESky4mmO/IPZrYYeA0oBqa5++aYVyZx9c+lhby+vIgbTplAdlpSvMsRERHpcqLpjjwf+DNwAXAv8LyZTYly+w68bGbzzWx2xPIrzGyhmf3ZzDL2tGiJrZr6Rn787GLGDuzLhRqMLyIiEhPRjLQ+CzjS3R9292uBy4H7otz+Ee5+EHAS8A0z+wzwO2AUwVGXm4BftfZAM5ttZvPMbF5RUVGUTyf7w+9eX8WGsmp+fPoknZpIREQkRnb7CevuZ7h7YcTtfwMzo9m4uxeEPwuBJ4GZ7r7F3RvdvQn446625e53u/sMd5+RlZUVzdPJfrCupIrfvbGK06YM4bBRGowvIiISK7sMYWb2aMT1n7e4+7ndbdjMUswstfk6cCKwyMwGR6x2JrBojyqWmLr5ucX0TDCu12B8ERGRmGprYP6YiOsnAD+IuB1N09RA4Ekza36eh9z9xXDKi6kE48XWApftUcUSM68u28I/lxZy7UnjGZSuwfgiIiKx1FYI8728L1jBfTXwHwP43f38KOqSdlZT38iPnlnCqKwULjpiRLzLERER6fLaCmHJZjaNoMuyT3jdwkuf9ihO2s/db65mXWkVD15yCL16aDC+iIhIrLUVwjYBvw6vb4643nxbuoj1pVX89rWVnHLgYI4YPSDe5YiIiHQLuwxhOmdk9/GT55aQYMb1p2gwvoiISHtRv1M3N3dlMS8v2cIVs0YzpJ96mUVERNqLQlg31tTk/PT5pQzt14eLj9RgfBERkfakENaNPfXRRhYXbOP7nxtHUs/EeJcjIiLSrURz7kgzs6+Y2Y3h7Vwzi2rGfOm4auobue2l5UzOSee0yUPiXY6IiEi3E01L2P8BhwHnhbe3A7+NWUXSLv789hoKymu47uQJJCRYvMsRERHpdtqaoqLZIe5+kJl9CODuZWbWK8Z1SQyVVNTyf6+t4oSJAzl0pM4PKSIiEg/RtITVm1ki4Sz5ZpYFNMW0Komp2+esoLq+kWtOGh/vUkRERLqtaELYHcCTQLaZ/RR4C/hZTKuSmFlVVMGD763jyzNzGZXVN97liIiIdFu77I40sxHuvsbdHzSz+cBxBKcsOsPdl7ZbhbJf3frCMvr0TORbx4/Z/coiIiISM22NCXscmG5mc9z9OGBZO9UkMfLu6hJeWbKFqz87jgF9e8e7HBERkW6trRCWYGY3AWPN7Lst73T3X7fyGOmgmpqcnz2/lMHpSZqYVUREpANoa0zYuUANQVBLbeUincizCwtYuKGcqz+riVlFREQ6grZO4L0c+LmZLXT3F9qxJtnPauob+cWLyzlgSBpnTB0a73JERESEtgfmf8XdHwAmmtmElverO7LzuHfuWjZureaXZ0/WxKwiIiIdRFtjwlLCn63NY+AxqEVioLSyjt++upLjxmdz+OgB8S5HREREQm11R/4h/PnjlveZ2bdjWZTsP3fMWUFVfSPXnqyJWUVERDqSaCZrbc1/HC0pHc/qogoeeDefLx08jNHZOpZCRESkI9nbEKaBRZ3Az19cRu8eCXzn+LHxLkVERERa2NsQpjFhHdy/15Ty0uIt/Pcxo8hK1cSsIiIiHU1bR0dup/WwZUCfmFUk+6ypyfnpP5YwKC2Ji48cGe9yREREpBVtDczXIKJO6tmFBSzYUM5t50yhTy9NzCoiItIR7W13pHRQzROzThycxhemaWJWERGRjkohrIu5L5yY9YZTJmhiVhERkQ5MIawLKa2s467XVjJLE7OKiIh0eAphXcgdc1ZQWdvAtSdpYlYREZGOTiGsi1hTXMkD7+Zz7sxcxgzUMRUiIiIdXVvnjtxnZrYW2A40Ag3uPsPM+gOPAMOBtcAX3b0slnV0B7e+sJTePRL49vFj4l2KiIiIRKE9WsKOdfep7j4jvH0NMMfdxwBzwtuyD5onZr386FFkpybFuxwRERGJQjy6Iz8P3Bdevw84Iw41dBmRE7NecpQmZhUREeksYh3CHHjZzOab2exw2UB33wQQ/syOcQ1d2nMfb2LBhnKu+uw4TcwqIiLSicR0TBhwhLsXmFk28IqZLYv2gWFomw2Qm5sbq/o6tZr6Rn7+wjImDk7jTE3MKiIi0qnEtCXM3QvCn4XAk8BMYIuZDQYIfxbu4rF3u/sMd5+RlZUVyzI7reaJWa8/ZQKJmphVRESkU4lZCDOzFDNLbb4OnAgsAp4BLgxXuxB4OlY1dGUlFbXc9dpKjh2XxRGamFVERKTTiWV35EDgSTNrfp6H3P1FM3sfeNTMLgbWAefEsIYu69YXllFd18j1p0yIdykiIiKyF2IWwtx9NTClleUlwHGxet7u4P21pTw2fwOXHz2K0dmamFVERKQz0oz5nUx9YxM3PLmIIelJfPO40fEuR0RERPZSrI+OlP3svrlrWb5lO384fzrJvfT2iYiIdFZqCetENpVX85tXPmHW+GxOnDgw3uWIiIjIPlAI60RueW4pDU3Oj047gPCABxEREemkFMI6iTc/KeIfH2/iimNHk5uZHO9yREREZB8phHUCNfWN3Pj0IkYMSGH20To/pIiISFegkd2dwN1vrmZtSRX3XzyT3j10fkgREZGuQC1hHVx+SSV3vbaSUycP5qgxOn2TiIhIV6EQ1oG5Oz96ZjG9EhP44akT412OiIiI7EcKYR3YS4u38NryIr5zwlgGpiXFuxwRERHZjxTCOqjK2gZufnYx4welcuFhefEuR0RERPYzhbAO6o5XV1BQXsNPz5xEj0S9TSIiIl2NPt07oEUby7nnX2v40oxhTM/rH+9yREREJAYUwjqYitoGrnz4Qwb07c01J42PdzkiIiISI5onrIO58alF5JdU8tClh5KR0ive5YiIiEiMqCWsA3l8/gae+HAj3zxuDIeOzIx3OSIiIhJDCmEdxKqiCn741CIOGdGfK2eNiXc5IiIiEmMKYR1ATX0j33jwA/r0SuT2c6eRmGDxLklERERiTGPCOoCfPb+UZZu38+evzmBQuiZlFRER6Q7UEhZnLy7azF/fyeeSI0cwa/zAeJcjIiIi7UQhLI42lFXx/ccXMDknne9/TtNRiIiIdCcKYXFS39jENx/+kCaHO8+bRq8eeitERES6E40Ji5PfvPIJH6zbyp3nTSMvMyXe5YiIiEg7U/NLHPxrRRG/e2MV5x48jNOmDIl3OSIiIhIHCmHtbFN5Nd95ZAGjs/py02kHxLscERERiROFsHa0qbyac+9+l9r6Ru768kH06ZUY75JEREQkThTC2knB1iCAlVbUcd/FMxk3KDXeJYmIiEgcaWB+O2gOYGWVdfz14plMy82Id0kiIiISZ2oJizEFMBEREWlNzEOYmSWa2Ydm9lx4+14zW2NmH4WXqbGuIV42RgSw+y85RAFMREREdmiP7shvAUuBtIhlV7v74+3w3HETBLB32FpVz/2XHMLUYf3iXZKIiIh0IDFtCTOzHOAU4E+xfJ6OJjKAPXCxApiIiIj8p1h3R/4v8H2gqcXyn5rZQjP7jZn1bu2BZjbbzOaZ2byioqIYl7n/bCir2imATVEAExERkVbELISZ2alAobvPb3HXtcB44GCgP/CD1h7v7ne7+wx3n5GVlRWrMverJQXbOO+P7yqAiYiIyG7FckzYEcDpZnYykASkmdkD7v6V8P5aM/sLcFUMa2gXFbUN/OaVT7h37loyknvy4CWHMDlHAUxERER2LWYhzN2vJWj1wsyOAa5y96+Y2WB332RmBpwBLIpVDbHm7rywaDM3P7uELdtrOG9mLj/47HjSk3vGuzQRERHp4OIxWeuDZpYFGPARcHkcathn+SWV3Pj0Yt74pIiJg9P43VcO0hQUIiIiErV2CWHu/jrwenh9Vns8Z6zUNjTyhzdW89vXVtIjwfjhqRO58LA8eiRq3lsRERGJnk5btAfeXlnMD59axOriSk45cDA/PHUig9KT4l2WiIiIdEIKYW2orG3gvTUlvPlJMf9aUcSqokryMpO596KDOWZcdrzLExERkU5MISxCU5OzZNM23lxRxJufFDE/v4z6RiepZwKHjMjk/EPzOHdmLkk9E+NdqoiIiHRyCmHAu6tLeOi9dby1spjSyjoAJg5O42tHjuAzY7KYnpeh4CUiIiL7lUIYsK60irmrSjhmbBZHjR3AkaOzyEptdSJ/ERERkf1CIQw4c9pQzpmeQzB1mYiIiEjsKYQBPTW9hIiIiLQzpQ8RERGROFAIExEREYkDhTARERGROFAIExEREYkDhTARERGROFAIExEREYkDhTARERGROFAIExEREYkDhTARERGROFAIExEREYkDc/d417BbZlYE5Mf4aQYAxTF+DgloX7cP7ef2o33dfrSv24f2877Jc/es3a3UKUJYezCzee4+I951dAfa1+1D+7n9aF+3H+3r9qH93D7UHSkiIiISBwphIiIiInGgEPapu+NdQDeifd0+tJ/bj/Z1+9G+bh/az+1AY8JERERE4kAtYSIiIiJxoBAmIiIiEgcKYSJdnJlZvGsQEZH/pDFhLZiZuXZKu9C+bn/a57Gl/Rs7ZpYJjAFqgDRgpbsXxLeqrsfMRhLs5yQgBZjn7p/Et6quSyGsDfqH2n60r/cfM0sADgcGA/2ArfD/7Z1prJ1VFYaft5VRQJBJ21KqCAWUsYAShmIhglBQMMgUNDIEMQKRKWEQGUVbSmQQS2USqVRBkUlEbEuDBWzLKLQWKBCgTKVAkaGUtq8/1i4eiRCTe+7dt99Zz69zz5fcvFnZZ+/1rb0G7rY9u6qwDiPXdPuQtDwwFugPPAW8CLwFPGJ7XE1tTULSCsCNwGLgH4CJPeQp4DLbcyrKayQd74RJ6gvsAQwA1iF+3LfafqKqsAaStu4ZJH0LOIA4pKYBawMrAPfavqqitMaRa7pnkHQksLvtPSWtCWwIbA4MAV4ATrf9bk2NTUDSEcCetodLWpXYOz4H7EhExk62/VZNjU3jY7UF9AIOBg4C7gNmED/sUZIeAi5Kz7+tpK17hu8BJ9meWK5wPg1sBOwjaXnbo+vKaxS5pnuGl4FXJK1QbDoHuEvSBsB5hJNwR02BDeEx4DVJq9ueS0TRZ0qaBlwM7A1cU1Ng08hImDQJGGH7VkkfJ7z9zxKb67O2R1YV2CDS1t1PScI/EVgRONf2/JZnmwIjgaMyx6M95JruGSR9EhgNvAlMBB4AZtp+T9LNwE22f1lTYxOQtCJwETAQuBOYRETQF0q6CbjFdjZxbSPphEmHAhsAZ7aGWSX1A34DHG97Wi19TSJt3TNIWp/YSOcBk4HJtu8rz14GBtl+u6LExiDpEE+FgVQAAAgMSURBVGAwuaZ7BEmHA+sRtzhrAqsBKwHDc023D0m7EHml6xIvFe8Ci4B9087tJZ0waR3gAmKxjQf+ZPtOSasAM4H1ctG1B0kDgEuI5NrxRO7MpLR19yBpX2BLoB+RO/M4UVF2QlVhDULSQOBCInIwgYgU5P7RRiT1J658ZxDXZWsQzteC8vlvtp+pp7B5lIh6P2B1Ijl/IGHneVWFNZCOdsIk9bG9uHzeGtgV2BbYmIggzLL9o4oSG4OkwbZnls9bA7sBOxBJn5OBJ9PWXaNEX74LbEZckU2WtBaxmc4HliOqyRZVlNlIJA0BdgaGEdHeXNNtoOwVPwPuAbYm1vFDwM2276qprUmUl4m9gIeB6bZfqSypY+hYJ0zSNsCPiTD2/PLd2kRp7jLAsrafrqewOUjaCpgCnE84By+X/JkFhIPgfJPtOpIuJQ6pucDngb8CmxCl5pdlu4T2USJdOxIvbtOAp4GXgFlEpaRz/+g6kkYBr9k+u/z9GWBf4NvAVOAw2wsrSmwEkq4mXt5uARYCTwIzbE8pxT0H2L64psam0slO2AXAPNunSdoC2J8IbT8HjMmeSu1D0k+ICr1XgeczWbl7kDQVGGr7bUmPA78j8sKGAs8Cx+bVWHuQ9AvgHeB5okVFP2AccLvtu2tqaxKSjgW2AX4IPG37vZZnVwOX2p5cS19TkPR74CZinxhC7Nd9gX8C3wDm2t6vnsLm0slO2B+Bs2zfJ+kWouJmNnEduRJwnO3Xa2psCpLuB3YnnNzRxI/7FMLmfYFFGaXpGqV30sXAI8RGeo7t/i3PJwEH2X6uksRGIWkKsNMSp1bSOKKNwk5Ekv71FeU1BknLAKcTeUnjgSeAhbZfkDQTONj2lIoSl3pKc+eBRFDitfLdIGArYBDhAA+1/WAliY2mk/uEXQWMkHQe0Mf2qPL9OEl3EYmf6YR1EUnbAc/YfpFoZLm9pOOA/YAXbU+vKrAh2J5Trm6OIJJp7y2J+X8hqps+kQ5Ye5C0BnH9eLCkW4lr9UG295e0GXCEpFttv1NT59LMkmkDpQXFOcDhwAlENH2upC8Q43TSAesCxc6LifX8PuUq/WlJOwDz0wHrPjoyEiapr+1Fko4mkmg3IZotXkFEwa6wvXFNjU1B0mDCyZ0hacVyVbYa8AOiqei5LQ5w0iZKT7DDifzGjYDf2r6krqrmUA6nQwgHbABwX0lt2BY4z/Z2VQUu5ZRJBF8irtJXBq61/bCkTYjWFLOBObZfrShzqafFzjsRV5B/sD2h5Xl/YHDrd0l76UgnrBVJQ4FdiHDsp4gRGONs/7mqsIbSOk9P0jCiACJt3QVK1/DBRMXpm8DYcmANISp9/07k0yyoKLNxlCub9Ygrsrm235R0DRH5PbmmtqWd0n/tEGJeZD/gQEDElftltt+oKK8xtNj5GuL8O5AYcXYFcGHpmp90Ix3nhJUDawMimfZ14gc9S9KytheUsS7zP/q/JP8PxdYbAl8F3gautP1Iy3NBlJHVUdgMJN1IVEBOBE4mmiw+SMx5m1hTW9NocXh3JUbnXLtk+kCJKmxDVJVlKkMXkHQ7MNr2DS3fDQGOJq4hL6omrkF8iJ23BL4P3J8Vkd1Pn9oCKjCSCL9eD3wRmC7pHmC30jcsHbD2MZI4lK4jyp+nSpoqaZ+WnI90wLpAaYC7vu1TbY8HDgVGEU1xDy7NiJP2MZLYN24grnAeljRN0t62F9m+Jx2wrlFeziYQe8b7OKY+nAh8s7S9SbrAR9j5fuAkYL+0c/fTUYn5LQfW18rfs4DDiM7WexHzyJ6tp7A5/A9bP0nYegYRGZtK2rod9AGmSBpQEu/XBba3fWrpqXSkpFPS2e06H7F/zAB2lzTNdq7pLmLbksYAYyRNAK4l8nQXETNR1wIeramxCaSdewedFgl7/8Aqfy85sH4NPEMcWKqmrll8mK3HEs5X2ro9PAs8BTxa2lB8h8ijgchvHJQOWNvINd0DlL6NuwBjgCuJHo7PSboOOIMY1p2Vp10k7dw76KhIGP99YD1YPrceWF/OA6ttpK17gGLDM0rz4T2AO1saDQ8HLq8mrnnkmu5mSj7ST4lh0e8Aj9neufTB25yIzLxQUWIjSDv3HjouMR9A0qp84MAqyc2X276pqriGkbbuPiQdQwxDH2v7oQ886w8MK1HepI3kmu4+JP2cmNl7vmLu6SVEe5XrJK0M7GP7V3VVLv2knXsPHXMdKekYSSMkbWb7ddtjWzbQ/sD1uYG2h7R1j3Ey0VrlMkmTJB1b7AuwBZBDeNtErukeYwvgbgDbLxORxkPLs6OALSvpahpp515Cx0TCJL1ElPCvR7RLuJHw/GdLGk6MzrmtpsamkLbufkoT3BHAQcBywDDg68CmwHSi0GRodhRvD7mmu5/S4mM74KnWAgfFXMM7iMHdx9t+oJLERpB27l10hBOWB1bPkbbuOSQtD9DaVkXSKsBZwFdsb1RLW5PINd2z6D8TTfrYXixpfeA2YrbhkNr6mkLauXfQEU4Y5IHVk6St67Ck95pihuQ822fW1tQUck3XocVROBt4KZu0dg9p53p0THXkB5uwlgPrDUkLif4oSZtIW9ehpTLvQmLQcdImck3XofSsAjitqpCGk3auR8dEwj4MSesCr9r+V20tTSdtnTSNXNNJknSFjnfCkiRJkiRJatAxLSqSJEmSJEl6E+mEJUmSJEmSVCCdsCRJkiRJkgqkE5YkSZIkSVKBdMKSJEmSJEkq8G+61bPBNHTv3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "data = np.genfromtxt('life-expectancy-china-1960-2016.txt',\n",
    "                     delimiter=',',\n",
    "                     names=['x', 'y'])\n",
    "da1960  = data[0][1]\n",
    "da2016  = data[len(data)-1][1]\n",
    "increase = (da2016 - da1960)/da1960\n",
    "note = 'from {:.2f} in 1960 to {:.2f} in 2016, increased  {:.2%}'\\\n",
    "    .format(da1960, da2016, increase)\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(data['x'], data['y'])\n",
    "plt.ylabel('Life Expectancy from Birth')\n",
    "plt.tick_params(axis='x', rotation=70)\n",
    "plt.title('CHINA\\n' + note)\n",
    "\n",
    "# plt.savefig('life-expectancy-china-1960-2016.png', transparent=True)\n",
    "plt.show()\n",
    "\n",
    "# data from:\n",
    "# https://databank.worldbank.org/data/reports.aspx?source=2&series=SP.DYN.LE00.IN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "如此发展下去，虽然人类不大可能永生不死，但平均寿命依然在持续延长是个不争的事实。与上一代不同，现在的千禧一代，需要面对的是百岁人生 —— 毫无疑问，不容置疑。\n",
    "\n",
    "这么长的人生，比默认的想象中可能要多出近一倍的人生，再叠加上另外一个因素 —— 这是个变化越来越快的世界 —— 会是什么样子？\n",
    "\n",
    "我是 1972 年出生的。从交通工具来看，我经历过出门只能靠步行，大街上都是牛车马车，机动车顶多见过拖拉机，到有自行车，到见过摩托车，到坐小汽车，到自己开车，到开有自动辅助驾驶功能的电动车…… 从阅读来看，我经历过只有新华书店，到有网络上的文字，到可以在当当上在线买到纸质书，到有了国际信用卡后可以在 Amazon 上第一时间阅读新书的电子版、听它的有声版，到现在可以很方便地获取最新知识的互动版，并直接参与讨论…… 从技能上来看，我经历过认为不识字是文盲，到不懂英语是文盲，到不懂计算机是文盲，到现在，不懂数据分析的基本与文盲无异……\n",
    "\n",
    "我也见识过很多当年很有用很赚钱很令人羡慕的技能“突然”变成几乎毫无价值的东西，最明显的例子是驾驶。也就是二十多年前，的哥还是很多人羡慕的职业呢！我本科的时候学的是会计专业，那时候我们还要专门练习打算盘呢！三十年之后的今天，就算有人打算盘打得再快，有什么具体用处嘛？我上中学的时候，有个人靠出版字帖赚了大钱 —— 那时候据说只要写字漂亮就能找到好工作；可今天，写字漂亮与否还是决定工作好坏的决定性因素吗？打印机很便宜啊！\n",
    "\n",
    "这两个因素叠加在一起的结果就是，这世界对很多人来说，其实是越来越残忍的。\n",
    "\n",
    "我见过太多的同龄人，早早就停止了进步，早早就被时代甩在身后，早早就因此茫然不知所措 —— 早早晚晚，你也会遇到越来越多这样的人。他们的共同特征只有一个：\n",
    "\n",
    "> 没有自学能力\n",
    "\n",
    "有一个统计指数，叫做人类发展指数（Human Development Index），它的曲线画出来，怎么看都有即将成为指数级上升的趋势。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "lebdata = np.genfromtxt('life-expectancy-china-1960-2016.txt',\n",
    "                     delimiter=',',\n",
    "                     names=['x', 'y'])\n",
    "\n",
    "hdidata = np.genfromtxt('hdi-china-1870-2015.txt',\n",
    "                     delimiter=',',\n",
    "                     names=['x', 'y'])\n",
    "\n",
    "\n",
    "plt.plot(hdidata['x'], hdidata['y'])\n",
    "plt.tick_params(axis='x', rotation=70)\n",
    "plt.title('China: 1870 - 2015')\n",
    "\n",
    "# plt.savefig('human-development-index.png', transparent=True)\n",
    "\n",
    "plt.legend(['Human Development Index'])\n",
    "plt.plot(lebdata['x'], lebdata['y']*0.005)\n",
    "plt.plot(secondary_y=True)\n",
    "# plt.savefig('human-development-index-china-1870-2015.png', transparent=True)\n",
    "\n",
    "plt.show()\n",
    "# data from:\n",
    "# https://ourworldindata.org/957e133e-f3e9-4bf2-9627-dbf30ebc9b4d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "社会发展越来越快，你要面对的人生越来越长，在那一段与你的直觉猜想并不相同的漫漫人生路上，你居然没有磨练过自学能力，竟然只能眼睁睁地看着自己被甩下且无能为力，难道接下来要在那么长的时间里“苦中作乐”吗？\n",
    "\n",
    "没有未来的日子，怎么过呢？\n",
    "\n",
    "我本科学的是会计，研究生跑到国外读宏观经济学没读完，跑回国内做计算机硬件批发，再后来去新东方应聘讲授托福课程，离开新东方之后创业，再后来做投资，这期间不断地写书…… 可事实上，我的经历在这个时代并不特殊。有多少人再后来的职业生涯中所做的事情与当年大学里所学的专业相符呢？\n",
    "\n",
    "纽约联邦储蓄银行在 2012 年做过一个调查，发现人们的职业与自己大学所学专业相符的比例连 _30%_ 都不到。而且，我猜，这个比例会持续下降的 —— 因为这世界变化快，因为大多数教育机构与世界发展脱钩的程度只能越来越严重……"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "labels = ['Major Match', '']\n",
    "sizes = [273, 727]\n",
    "colors = ['#E2E2E2', '#6392BF']\n",
    "explode = (0, 0.08)\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.pie(sizes,\n",
    "        labels=labels,\n",
    "        explode=explode,\n",
    "        autopct='%1.1f%%',\n",
    "        colors=colors,\n",
    "        startangle=270,\n",
    "        shadow=True)\n",
    "# plt.savefig('major-match-job.png', transparent=True)\n",
    "plt.show()\n",
    "\n",
    "# data from:\n",
    "# https://libertystreeteconomics.newyorkfed.org/2013/05/do-big-cities-help-college-graduates-find-better-jobs.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "绝大多数人终生都饱受**时间幻觉**的拖累。\n",
    "\n",
    "小时候觉得时间太长，那是幻觉；长大了觉得时间越来越快，那还是幻觉 —— 时间从来都是匀速的。最大的幻觉在于，总是以为“时间不够了” —— 这个幻觉最坑人。许多年前，有一次我开导我老婆。她说，“啊？得学五年才行啊？！太长了！” 我说，\n",
    "\n",
    "> “你回头看看呗，想想呗，五年前你在做什么？是不是回头一看的时候，五年前就好像是昨天？道理是一样的，五年之后的某一天你回头想今天，也是‘一转眼五年就过去’了…… 只不过，你今天觉得需要时间太多，所以不肯学 —— 但是，不管你学还是不学，五年还是会‘一转眼就过去’的…… 到时候再回头，想起这事儿的时候，没学的你，一定会后悔 —— 事实上，你已经有很多次后悔过‘之前要是学了就好了’，不是吗？”\n",
    "\n",
    "现在回头看，开导是非常成功的。十多年后的今天，她已经真的可以被称为“自学专家” —— 各种运动在她那儿都不是事儿。健身，可以拿个北京市亚军登上健与美杂志封面；羽毛球，可以参加专业比赛；潜水，潜遍全球所有潜水胜地，到最后拿到的各种教练证比她遇到的各地教练的都多、更高级；帆船，可以组队横跨大西洋；爬山，登上喜马拉雅……"
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    "都说，人要有一技之长。那这一技究竟应该是什么呢？\n",
    "\n",
    "> 自学能力是唯一值得被不断磨练的长技。\n",
    "\n",
    "磨练出自学能力的好处在于，无论这世界需要我们学什么的时候，我们都可以主动去学，并且还是马上开始 —— 不需要等别人教、等别人带。\n",
    "\n",
    "哪怕有很强的自学能力的意思也并不是说，什么都能马上学会、什么都能马上学好，到最后无所不精无所不通…… 因为这里有个时间问题。无论学什么，都需要耗费时间和精力，与此同时更难的事情在于不断填补耐心以防它过早耗尽。另外，在极端的情况下，多少也面临天份问题。比如身高可能影响打篮球的表现，比如长相可能影响表演的效果，比如唱歌跑调貌似是很难修复的，比如有些人的粗心大意其实是基因决定的，等等。不过，以我的观察，无论是什么，哪怕只是学会一点点，都比不会强。哪怕只是中等水平，就足够应付生活、工作、养家糊口的需求。\n",
    "\n",
    "我在大学里学的是会计专业，毕业后找不到对口工作，只好去做销售 —— 没人教啊！怎么办？自学。也有自学不怎么样的时候，比如当年研究生课程我就读不完。后来想去新东方教书 —— 因为听说那里赚钱多 —— 可英语不怎么样啊！怎么办？自学。离开新东方去创业，时代早就变了，怎么办？自学，学的不怎么样，怎么办？硬挺。虽然创业这事儿后来也没怎么大成，但竟然在投资领域开花结果 —— 可赚了钱就一切平安如意了吗？并不是，要面对之前从来没可能遇到的一些险恶与困境，怎么办？自学。除了困境之外，更痛苦的发现在于对投资这件事来说，并没有受过任何有意义的训练，怎么办？自学。觉得自己理解的差不多了，一出手就失败，怎么办？接着学。\n",
    "\n",
    "我出身一般，父母是穷教师。出生在边疆小镇，儿时受到的教育也一般，也是太淘气 —— 后来也没考上什么好大学。说实话，我自认天资也一般，我就是那种被基因决定了经常马虎大意的人。岁数都这么大了，情商也都不是一般的差 —— 还是跟年轻的时候一样，经常莫名其妙就把什么人给得罪透了……\n",
    "\n",
    "但，我过得一直不算差。\n",
    "\n",
    "靠什么呢？人么，一个都靠不上。到最后，我觉得只有一样东西真正可靠 —— **自学能力**。于是，经年累月，我磨练出了一套属于我自己的本领：只要我觉得有必要，我什么都肯学，学什么都能学会到够用的程度…… 编程，我不是靠上课学会的；英语，不是哪个老师教我的；写作，也不是谁能教会我的；教书，更没有上过师范课程；投资，更没人能教我 —— 我猜，也没人愿意教我…… 自己用的东西自己琢磨，挺好。\n",
    "\n",
    "关键在于，自学这事儿并不难，也不复杂，挺简单的，因为它所需要的一切都很朴素。\n",
    "\n",
    "于是，从某个层面上来看，我每天都过的很开心。为什么？因为我有未来。凭什么那么确信？因为我知道我自己有自学能力。\n",
    "\n",
    "**—— 我希望你也有。**\n",
    "\n",
    "准确地讲，希望你有个更好的未来。\n",
    "\n",
    "而现在我猜，此刻，你心中也是默默如此作想的罢。"
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